Cleaning Data in Python
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Learn to fix data types, ranges, duplicates, messy categories, uniformity and missing values in Python, then link records across datasets.
Dirty data leads to wrong conclusions, and fixing it in Python means spotting the problem before choosing a remedy. You work through columns stored with the wrong data type, values outside a sensible range, duplicated rows, messy category labels, mixed units and date formats, and gaps in the data, then merge two datasets whose entries are spelled differently by scoring how similar their strings are.
What You Will Achieve
- Convert columns to the correct data type and drop rows that break range or uniqueness rules
- Standardize category labels and text fields so the same value is always written the same way
- Make units, currencies and dates uniform, verify totals with cross-field validation, and choose how to treat missing values
- Link records across two datasets with string similarity scores, even when names contain typos
Program Overview
Data Types, Ranges and Duplicates
Starts with the most frequent problems: values stored as the wrong type, numbers outside a valid range, and rows that appear more than once.
- Data type constraints and conversion
- Range constraints, including future dates
- Finding and treating duplicate rows
Categories, Text, Uniformity and Missing Data
Covers the messier parts of a table, including category labels and strings, values recorded in different units or formats, cross-field checks and gaps.
- Membership constraints and inconsistent categories
- Remapping categories and cleaning text
- Uniform units, currencies and dates
- Cross-field validation and missing values
Record Linkage with String Similarity
Closes with merging datasets whose values contain typos or alternate spellings, ending in one clean master table of restaurant reviews.
- Minimum edit distance and cutoff points
- Generating and comparing record pairs
- Linking two restaurant review DataFrames
Certificate Awarded (Paid)
Complete the course to earn it.
Job Outlook
These cleaning skills are used wherever analysis depends on reliable tables, including data analysis, data science and data engineering work in Python.
Good for: Analysts and data scientists working in Python who want a repeatable routine for spotting and fixing dirty data before analysis.
Keep in mind: Missing data gets a few lessons inside one chapter, so expect a working introduction to patterns of missingness rather than a deep treatment of imputation.
Bottom line: Its closing exercise, merging two restaurant review datasets with typo-tolerant matching, ties the earlier cleaning steps into one realistic task.
Frequently Asked Questions
What is record linkage?
It is a way to merge datasets when the same entity is written with typos or different spellings. You score string similarity with minimum edit distance, set a cutoff, generate candidate pairs and link two restaurant review tables into one master dataset.
What uniformity problems does the course deal with?
The lessons cover weights recorded in pounds alongside kilograms, amounts in different currencies, and dates written in ambiguous or mixed formats, and show how to bring each column into a single standard.
Why does cleaning matter before analysis?
Dirty data produces unreliable and inaccurate conclusions. Problems can be caused by human error, a faulty sensor or corrupted files, so checking types, ranges, duplicates and consistency comes before any analysis.
Which Python libraries do the lessons use?
The cleaning functions belong to pandas and NumPy, and the lessons work on DataFrames, using assert statements to confirm that a fix has worked.
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